hierarchical learning in ai - general problem
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Mar 6, 2002 Jie Bao 1
Hierarchical Learning in AI
Notes 1
General Problem
Jie Bao
AI Lab, Iowa State University
baojie@cs.iastate.edu
Mar 6, 2002 Jie Bao 2
Content of whole series Hierarchy and any problem in general information system HL in GA
Delicate structure in coding string Behavior Learning Sociobiology and ecology competition
HL in NN Modular learning Ensemble learning Hybrid learning
HL in MAS(MultiAgent System) Society entity: an agent in the hierarchy Society relationship: their languages
Mar 6, 2002 Jie Bao 3
Why hierarchy? Nature system are mostly hierarchical
system “Divide and conquer” in Engineering The power and stableness from
cooperation of subsystems Easier to design and implement
Mar 6, 2002 Jie Bao 4
How to hierarchy? System theory: summation( all parts) < whole system The biologically hierarchical system are formed by
self-organization Start from simple system Evolve from combine simple system to complex
system The structural hierarchy is usually the result of
evolutionary history
Mar 6, 2002 Jie Bao 5
General Hierarchy Formation Direction: Order increasing Grow Multiplication Accelerate Interaction Higher level hierarchy appears! Differentiation Centralization
Mar 6, 2002 Jie Bao 6
Three kinds of order-increasing system with Hierarchy
They are all order-increasing They are derived from the former one They all have hierarchical structure
Many theory and algorithm can be borrowed between the science of them !
General Hierarchy Formation
Organism Society Automata
Mar 6, 2002 Jie Bao 7
General Hierarchy Formation
System is in the inner development of one level, for example, form ape to human. System search for favorable position on “niche” space, and the moving progress is called “Evolution”. In this progress, system tends to be more ordered because only more ordered system can have higher energy-consuming efficiency and win in the competition.
When the structure of system block in the way to higher order, hierarchy will be appeared. In this stage, a new high-level system “ bursts out”, such as society organized by human. Under the new organization, the system can have higher energy-consuming efficiency or energy-occupation ability, to further increase its order.
Mar 6, 2002 Jie Bao 8
Hierarchical Structure(1)--Organism
Body System Organ Tissue Cell Cell organelle Protein , nucleic acid ……
Mar 6, 2002 Jie Bao 9
Hierarchical Structure(2)--Society
Whole human world Nation State, Province City, County, Shire Community, Village Kin, Family Person
Mar 6, 2002 Jie Bao 10
Hierarchical Structure(3)--Automata
Cyber space: internet Local area network Terminal (both software and hardware) Module ( eg. CPU, operating system) Sub-module (eg. ALU, disk system) Smaller module, (eg. Adder, a interrupt
service routine) Bit operation(eg. Gate )
Mar 6, 2002 Jie Bao 11
Hierarchical Evolution(1)--Organism
Little molecule-> big molecule: 3.5 billion y Molecule -> cell (2 billion y?) Prokaryote -> Eukaryotic (? billion y ) Cell -> Multi-cellular (0.6 billion y) Multi-cellular -> society ( for human, 4
million y, for insects, 0.2 billion y?) Society -> Gaia Cell ( now )
Mar 6, 2002 Jie Bao 12
Hierarchical Evolution(2)--Society
Vassals and tribes in China
2000BC 10,000
1600BC 3,000
1000BC 800
700BC 140
400BC 10
221BC 1
Mar 6, 2002 Jie Bao 13
Hierarchical Evolution(3)--Automata
Operation system: Windows CP/L(?K) : late 1970s DOS(1M): more interrupts Windows 3.1(15M): GUI Windows 95(100M): multi-media IE, Plus, DirectX, ActiveX : Windows 98
(200M) Windows 2000(1G) : various fanciness
Mar 6, 2002 Jie Bao 14
Hierarchy in AI: simulate the nature
AI system
Immune system
Molecule
Cell
Multi-cell
SocietyHierarchy
Evolution
Gene
neural network
Mar 6, 2002 Jie Bao 15
The way to hierarchy(1): organism
Order increasing
Growth
Accelerate
interaction
Hierarchy
Differentiation
Centralization
Multiplication
Organism
The physical freedom of Molecule and cell are decreasing ( less entropy)
Growth of individual; the average body volume increase in evolution
Species are generated quicker and quicker
Isolated specie becomes “living fossil”
Differentiation of Tissue Organ
The evolution of neural system
Mitosis and amitosis
Mar 6, 2002 Jie Bao 16
The way to in hierarchy(2): Society
Order increasing
Growth
Accelerate
interaction
Hierarchy
Differentiation
Centralization
Multiplication
Society
Organization degree are increasing
Growth of total social economy
The acceleration in social development
Open society are developed quicker than isolated society
From individual, group, tribe to nation and international society
More detailed social professional work
Government and international organization
Culture split, language pedigree ;colonizztion
Mar 6, 2002 Jie Bao 17
The way to in hierarchy(3): OS
Order increasing
Growth
Accelerate
interaction
Hierarchy
Differentiation
Centralization
Multiplication
Process
and operating
system
Increase of OS size
Exponential growth of OS size
Message and signal between progress
The SDK of OS is composed by many application API
modules in OS are divided more detailedly
Central part controls all processes
The replication of progress and virus.
Mar 6, 2002 Jie Bao 18
The way to hierarchy(4): WAN
Order increasing
Growth
Accelerate
interaction
Hierarchy
Differentiation
Centralization
Multiplication
Wide area network
From unrestricted to be controlled and managed
The extend of network size
Rapid almost exponent growth
Protocol and messages
WAN, LAN, terminal
More and more different kind of websites
Formation of Portal, manage center, service center
?
Mar 6, 2002 Jie Bao 19
The way to hierarchy(5): NN
Order increasing
Growth
Accelerate
interaction
Hierarchy
Differentiation
Centralization
Multiplication
Neural Network
After training, input can converge to some attractors
(evolutionary neural network)
? Weights
Group network with complex behavior by neurons
From less structured to complex structured, such as layer
? ?
Mar 6, 2002 Jie Bao 20
The way to hierarchy(6): GA
Order increasing
Growth
Accelerate
interaction
Hierarchy
Differentiation
Centralization
Multiplication
Genetic Algorithm
Schema theorem
Fitness From individuals to population
Selection , crossover,mutation and new population
Mar 6, 2002 Jie Bao 21
The way to hierarchy(7): MAS
Order increasing
Growth
Accelerate
interaction
Hierarchy
Differentiation
Centralization
Multiplication
Multi-agent system
Converge to equilibrium point
Agent Language
Simple agent and complex agent society
Mar 6, 2002 Jie Bao 22
Hierarchical AI System: Social Computational intelligence: neural network,
multi-agent system (MAS), evolution computation and artificial immune system.
The basic idea of computational intelligence is “social computation”, that’s, complex intelligence can be obtained self-organizingly by simple intelligence individuals under some simple social rules (including competition, cooperation and so on).
Mar 6, 2002 Jie Bao 23
Hierarchical AI System: EcologicalSuch a “social computation” system can be regarded
as an hierarchical artificial ecology system, which has similar property and development to nature ecology system. So basic laws of computational intelligence can be regarded as “general ecology”.
Mar 6, 2002 Jie Bao 24
Hierarchical AI System: Self-organization
Some general laws in computational intelligence, such as order-increasing; information interchange; hierarchy structure and development; progressive centralization; progressive Differentiation, are in fact general properties of a kind of self-organizing systems.
Mar 6, 2002 Jie Bao 25
Hierarchical AI System: Self-organization(2)
Possible system
state space in early stage
Possible system state
space in early stage
Mar 6, 2002 Jie Bao 26
Hierarchical AI System: Self-organization(3)
Order
limited growth, stable or slow development.
break through developing obstacle by hierarchy with higher order but less freedom degree. More efficient in energy using and can use more energy that lower-level system can’t utilize
abundant resource stage , exponential growth
Mar 6, 2002 Jie Bao 27
Hierarchical AI System: interdisciplinary
Therefore, the development of hierarchical learning in computational intelligence, especially the hierarchical MAS, is closely related to the development of life sciences and social sciences. Neural Network (top-down) and MAS (bottom-up) are integrated methods to carry out the research in practice.
Mar 6, 2002 Jie Bao 28
Hierarchical learning in Neural Network Ensemble learning Modular learning Hybrid learning
Mar 6, 2002 Jie Bao 29
Hierarchical learning in GA Behavior evolution: hierarchical
structure in nonlinear coding (tree) Diversification of population Multi-level selection
Mar 6, 2002 Jie Bao 30
Hierarchical learning in MAS Evolution of cooperation by Reinforcement
learning Hierarchal Markov game: game between groups Hierarchical entity: agent and their society Hierarchical relationship: language
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